Skip to content

Updated onboarding docs #155

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Mar 27, 2024
+6 −2
Merged

Updated onboarding docs #155

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
dd67a79
Updated onboarding docs
mikelam-us-aixplain Mar 27, 2024
d3347e0
under -> undergoing
mikelam-us-aixplain Mar 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 6 additions & 2 deletions docs/user/user_doc.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,6 +58,8 @@ poll_url = start_response["url"]
poll_response = model.poll(poll_url)
```
### Deploying Hugging Face Large Language Models
This feature is currently undergoing maintenance.
<!---
You can deploy your very own Hugging Face large language models on our platform using the aiXplain SDK:
```console
$ aixplain onboard hf-model --name <what you'd like to name your model> --hf-repo-id <Hugging Face repository ID ({supplier}/{name})> --hf-token <Hugging Face token> [--api-key <TEAM_API_KEY>]
Expand All @@ -67,9 +69,11 @@ This command will return your model's ID. The on-boarding process will take 5 to
$ aixplain get hf-model-status --model-id <model ID> [--api-key <TEAM_API_KEY>]
```

Once the on-boarding process has completed, you can use this newly-deployed large language model just like any other model on our platform. Note that our platform currently only supports language models up 7 billion parameters in size (~30 GB), so any attempts to deploy larger models will result in an error message.
Once the on-boarding process has completed, you can use this newly-deployed large language model just like any other model on our platform. Note that our platform currently only supports language models up 7 billion parameters in size (~30 GB), so any attempts to deploy larger models will result in an error message.-->

### Uploading Models
This feature is currently undergoing maintenance.
<!---
In addition to exploring and running models, the aiXplain SDK allows you to upload your own models to the aiXplain platform. This requires a working model image in line with the template specified [here](https://github.com/aixplain/model-interfaces/blob/main/docs/user/model_setup.md). [These](https://github.com/aixplain/model-interfaces/tree/main) are the interfaces with which you will be working. You will also be required to have an aiXplain account as well as a TEAM_API_KEY which should be set either as an environment variable or passed into each of the following functions.

First, choose a hosting machine appropriate for your model. Note down the host machines "code". You can list the available hosting machines' specifications by running the following:
Expand Down Expand Up @@ -161,7 +165,7 @@ $ aixplain onboard model --model-id <model_id> --image-tag <model_image_tag> --i
```
`model-id` should be the model ID returned by the image-create-repo function used earlier. `image-tag` should be set to whatever string you used to tag your model image. The image sha256 hash can be obtained by running `docker images --digests`. Choose the hash corresponding to the image you would like onboarded.

This will send an email to an aiXplain associate to finalize the onboarding process.
This will send an email to an aiXplain associate to finalize the onboarding process. --->

## Pipelines
[Design](https://aixplain.com/platform/studio/) is aiXplain’s no-code AI pipeline builder tool that accelerates AI development by providing a seamless experience to build complex AI systems and deploy them within minutes. You can visit our platform and design your own custom pipeline [here](https://platform.aixplain.com/studio).
Expand Down